BOOK
Quantitative Analysis for Management, Global Edition
Barry Render | Ralph M. Stair | Michael E. Hanna | Trevor S. Hale
(2017)
Additional Information
Book Details
Abstract
For courses in management science and decision modeling.
Foundational understanding of management science through real-world problems and solutions
Quantitative Analysis for Management helps students to develop a real-world understanding of business analytics, quantitative methods, and management science by emphasizing model building, tangible examples, and computer applications. The authors offer an accessible introduction to mathematical models and then students apply those models using step-by-step, how-to instructions. For more intricate mathematical procedures, the 13th Edition offers a flexible approach, allowing instructors to omit specific sections without interrupting the flow of the material. Supporting computer software enables instructors to focus on the managerial problems and solutions, rather than spending valuable class time on the details of algorithms.
Table of Contents
| Section Title | Page | Action | Price |
|---|---|---|---|
| Cover | Cover | ||
| Title Page | 1 | ||
| Copyright Page | 2 | ||
| About the Authors | 3 | ||
| Brief Contents | 5 | ||
| Contents | 6 | ||
| Preface | 13 | ||
| Acknowledgments | 17 | ||
| Chapter 1: Introduction to Quantitative Analysis | 19 | ||
| 1.1. What Is Quantitative Analysis? | 20 | ||
| 1.2. Business Analytics | 20 | ||
| 1.3. The Quantitative Analysis Approach | 21 | ||
| Defining the Problem | 22 | ||
| Developing a Model | 22 | ||
| Acquiring Input Data | 22 | ||
| Developing a Solution | 23 | ||
| Testing the Solution | 23 | ||
| Analyzing the Results and Sensitivity Analysis | 24 | ||
| Implementing the Results | 24 | ||
| The Quantitative Analysis Approach and Modeling in the Real World | 24 | ||
| 1.4. How to Develop a Quantitative Analysis Model | 24 | ||
| The Advantages of Mathematical Modeling | 27 | ||
| Mathematical Models Categorized by Risk | 27 | ||
| 1.5. The Role of Computers and Spreadsheet Models in the Quantitative Analysis Approach | 27 | ||
| 1.6. Possible Problems in the Quantitative Analysis Approach | 30 | ||
| Defining the Problem | 30 | ||
| Developing a Model | 31 | ||
| Acquiring Input Data | 32 | ||
| Developing a Solution | 32 | ||
| Testing the Solution | 32 | ||
| Analyzing the Results | 33 | ||
| 1.7. Implementation—Not Just the Final Step | 33 | ||
| Lack of Commitment and Resistance to Change | 34 | ||
| Lack of Commitment by Quantitative Analysts | 34 | ||
| Summary | 34 | ||
| Glossary | 34 | ||
| Key Equations | 35 | ||
| Self-Test | 35 | ||
| Discussion Questions and Problems | 36 | ||
| Case Study: Food and Beverages at Southwestern University Football Games | 37 | ||
| Bibliography | 38 | ||
| Chapter 2: Probability Concepts and Applications | 39 | ||
| 2.1. Fundamental Concepts | 40 | ||
| Two Basic Rules of Probability | 40 | ||
| Types of Probability | 40 | ||
| Mutually Exclusive and Collectively Exhaustive Events | 41 | ||
| Unions and Intersections of Events | 43 | ||
| Probability Rules for Unions, Intersections, and Conditional Probabilities | 43 | ||
| 2.2. Revising Probabilities with Bayes’ Theorem | 45 | ||
| General Form of Bayes’ Theorem | 46 | ||
| 2.3. Further Probability Revisions | 47 | ||
| 2.4. Random Variables | 48 | ||
| 2.5. Probability Distributions | 50 | ||
| Probability Distribution of a Discrete Random Variable | 50 | ||
| Expected Value of a Discrete Probability Distribution | 50 | ||
| Variance of a Discrete Probability Distribution | 51 | ||
| Probability Distribution of a Continuous Random Variable | 52 | ||
| 2.6. The Binomial Distribution | 53 | ||
| Solving Problems with the Binomial Formula | 54 | ||
| Solving Problems with Binomial Tables | 55 | ||
| 2.7. The Normal Distribution | 56 | ||
| Area Under the Normal Curve | 58 | ||
| Using the Standard Normal Table | 58 | ||
| Haynes Construction Company Example | 59 | ||
| The Empirical Rule | 62 | ||
| 2.8. The F Distribution | 62 | ||
| 2.9. The Exponential Distribution | 64 | ||
| Arnold’s Muffler Example | 65 | ||
| 2.10. The Poisson Distribution | 66 | ||
| Summary | 68 | ||
| Glossary | 68 | ||
| Key Equations | 69 | ||
| Solved Problems | 70 | ||
| Self-Test | 72 | ||
| Discussion Questions and Problems | 73 | ||
| Case Study: WTVX | 79 | ||
| Bibliography | 79 | ||
| Appendix 2.1: Derivation of Bayes’ Theorem | 79 | ||
| Chapter 3: Decision Analysis | 81 | ||
| 3.1. The Six Steps in Decision Making | 81 | ||
| 3.2. Types of Decision-Making Environments | 83 | ||
| 3.3. Decision Making Under Uncertainty | 83 | ||
| Optimistic | 84 | ||
| Pessimistic | 84 | ||
| Criterion of Realism (Hurwicz Criterion) | 85 | ||
| Equally Likely (Laplace) | 85 | ||
| Minimax Regret | 85 | ||
| 3.4. Decision Making Under Risk | 87 | ||
| Expected Monetary Value | 87 | ||
| Expected Value of Perfect Information | 88 | ||
| Expected Opportunity Loss | 89 | ||
| Sensitivity Analysis | 90 | ||
| A Minimization Example | 91 | ||
| 3.5. Using Software for Payoff Table Problems | 93 | ||
| QM for Windows | 93 | ||
| Excel QM | 93 | ||
| 3.6. Decision Trees | 95 | ||
| Efficiency of Sample Information | 100 | ||
| Sensitivity Analysis | 100 | ||
| 3.7. How Probability Values Are Estimated by Bayesian Analysis | 101 | ||
| Calculating Revised Probabilities | 101 | ||
| Potential Problem in Using Survey Results | 103 | ||
| 3.8. Utility Theory | 104 | ||
| Measuring Utility and Constructing a Utility Curve | 104 | ||
| Utility as a Decision-Making Criterion | 106 | ||
| Summary | 109 | ||
| Glossary | 109 | ||
| Key Equations | 110 | ||
| Solved Problems | 110 | ||
| Self-Test | 115 | ||
| Discussion Questions and Problems | 116 | ||
| Case Study: Starting Right Corporation | 125 | ||
| Case Study: Toledo Leather Company | 125 | ||
| Case Study: Blake Electronics | 126 | ||
| Bibliography | 128 | ||
| Chapter 4: Regression Models | 129 | ||
| 4.1. Scatter Diagrams | 130 | ||
| 4.2. Simple Linear Regression | 131 | ||
| 4.3. Measuring the Fit of the Regression Model | 132 | ||
| Coefficient of Determination | 134 | ||
| Correlation Coefficient | 134 | ||
| 4.4. Assumptions of the Regression Model | 135 | ||
| Estimating the Variance | 137 | ||
| 4.5. Testing the Model for Significance | 137 | ||
| Triple A Construction Example | 139 | ||
| The Analysis of Variance (ANOVA) Table | 140 | ||
| Triple A Construction ANOVA Example | 140 | ||
| 4.6. Using Computer Software for Regression | 140 | ||
| Excel 2016 | 140 | ||
| Excel QM | 141 | ||
| QM for Windows | 143 | ||
| 4.7. Multiple Regression Analysis | 144 | ||
| Evaluating the Multiple Regression Model | 145 | ||
| Jenny Wilson Realty Example | 146 | ||
| 4.8. Binary or Dummy Variables | 147 | ||
| 4.9. Model Building | 148 | ||
| Stepwise Regression | 149 | ||
| Multicollinearity | 149 | ||
| 4.10. Nonlinear Regression | 149 | ||
| 4.11. Cautions and Pitfalls in Regression Analysis | 152 | ||
| Summary | 153 | ||
| Glossary | 153 | ||
| Key Equations | 154 | ||
| Solved Problems | 155 | ||
| Self-Test | 157 | ||
| Discussion Questions and Problems | 157 | ||
| Case Study: North–South Airline | 162 | ||
| Bibliography | 163 | ||
| Appendix 4.1: Formulas for Regression Calculations | 163 | ||
| Chapter 5: Forecasting | 165 | ||
| 5.1. Types of Forecasting Models | 165 | ||
| Qualitative Models | 165 | ||
| Causal Models | 166 | ||
| Time-Series Models | 167 | ||
| 5.2. Components of a Time-Series | 167 | ||
| 5.3. Measures of Forecast Accuracy | 169 | ||
| 5.4. Forecasting Models—Random Variations Only | 172 | ||
| Moving Averages | 172 | ||
| Weighted Moving Averages | 172 | ||
| Exponential Smoothing | 174 | ||
| Using Software for Forecasting Time Series | 176 | ||
| 5.5. Forecasting Models—Trend and Random Variations | 178 | ||
| Exponential Smoothing with Trend | 178 | ||
| Trend Projections | 181 | ||
| 5.6. Adjusting for Seasonal Variations | 182 | ||
| Seasonal Indices | 183 | ||
| Calculating Seasonal Indices with No Trend | 183 | ||
| Calculating Seasonal Indices with Trend | 184 | ||
| 5.7. Forecasting Models—Trend, Seasonal, and Random Variations | 185 | ||
| The Decomposition Method | 185 | ||
| Software for Decomposition | 188 | ||
| Using Regression with Trend and Seasonal Components | 188 | ||
| 5.8. Monitoring and Controlling Forecasts | 190 | ||
| Adaptive Smoothing | 192 | ||
| Summary | 192 | ||
| Glossary | 192 | ||
| Key Equations | 193 | ||
| Solved Problems | 194 | ||
| Self-Test | 195 | ||
| Discussion Questions and Problems | 196 | ||
| Case Study: Forecasting Attendance at SWU Football Games | 200 | ||
| Case Study: Forecasting Monthly Sales | 201 | ||
| Bibliography | 202 | ||
| Chapter 6: Inventory Control Models | 203 | ||
| 6.1. Importance of Inventory Control | 204 | ||
| Decoupling Function | 204 | ||
| Storing Resources | 205 | ||
| Irregular Supply and Demand | 205 | ||
| Quantity Discounts | 205 | ||
| Avoiding Stockouts and Shortages | 205 | ||
| 6.2. Inventory Decisions | 205 | ||
| 6.3. Economic Order Quantity: Determining How Much to Order | 207 | ||
| Inventory Costs in the EOQ Situation | 207 | ||
| Finding the EOQ | 209 | ||
| Sumco Pump Company Example | 210 | ||
| Purchase Cost of Inventory Items | 211 | ||
| Sensitivity Analysis with the EOQ Model | 212 | ||
| 6.4. Reorder Point: Determining When to Order | 212 | ||
| 6.5. EOQ Without the Instantaneous Receipt Assumption | 214 | ||
| Annual Carrying Cost for Production Run Model | 214 | ||
| Annual Setup Cost or Annual Ordering Cost | 215 | ||
| Determining the Optimal Production Quantity | 215 | ||
| Brown Manufacturing Example | 216 | ||
| 6.6. Quantity Discount Models | 218 | ||
| Brass Department Store Example | 220 | ||
| 6.7. Use of Safety Stock | 221 | ||
| 6.8. Single-Period Inventory Models | 227 | ||
| Marginal Analysis with Discrete Distributions | 228 | ||
| Café du Donut Example | 228 | ||
| Marginal Analysis with the Normal Distribution | 230 | ||
| Newspaper Example | 230 | ||
| 6.9. ABC Analysis | 232 | ||
| 6.10. Dependent Demand: The Case for Material Requirements Planning | 232 | ||
| Material Structure Tree | 233 | ||
| Gross and Net Material Requirements Plans | 234 | ||
| Two or More End Products | 236 | ||
| 6.11. Just-In-Time Inventory Control | 237 | ||
| 6.12. Enterprise Resource Planning | 238 | ||
| Summary | 239 | ||
| Glossary | 239 | ||
| Key Equations | 240 | ||
| Solved Problems | 241 | ||
| Self-Test | 243 | ||
| Discussion Questions and Problems | 244 | ||
| Case Study: Martin-Pullin Bicycle Corporation | 252 | ||
| Bibliography | 253 | ||
| Appendix 6.1: Inventory Control with QM for Windows | 253 | ||
| Chapter 7: Linear Programming Models: Graphical and Computer Methods | 255 | ||
| 7.1. Requirements of a Linear Programming Problem | 256 | ||
| 7.2. Formulating LP Problems | 257 | ||
| Flair Furniture Company | 258 | ||
| 7.3. Graphical Solution to an LP Problem | 259 | ||
| Graphical Representation of Constraints | 259 | ||
| Isoprofit Line Solution Method | 263 | ||
| Corner Point Solution Method | 266 | ||
| Slack and Surplus | 268 | ||
| 7.4. Solving Flair Furniture’s LP Problem Using QM for Windows, Excel 2016, and Excel QM | 269 | ||
| Using QM for Windows | 269 | ||
| Using Excel’s Solver Command to Solve LP Problems | 270 | ||
| Using Excel QM | 273 | ||
| 7.5. Solving Minimization Problems | 275 | ||
| Holiday Meal Turkey Ranch | 275 | ||
| 7.6. Four Special Cases in LP | 279 | ||
| No Feasible Solution | 279 | ||
| Unboundedness | 279 | ||
| Redundancy | 280 | ||
| Alternate Optimal Solutions | 281 | ||
| 7.7. Sensitivity Analysis | 282 | ||
| High Note Sound Company | 283 | ||
| Changes in the Objective Function Coefficient | 284 | ||
| QM for Windows and Changes in Objective Function Coefficients | 284 | ||
| Excel Solver and Changes in Objective Function Coefficients | 285 | ||
| Changes in the Technological Coefficients | 286 | ||
| Changes in the Resources or Right-Hand-Side Values | 287 | ||
| QM for Windows and Changes in Right-Hand- Side Values | 288 | ||
| Excel Solver and Changes in Right-Hand-Side Values | 288 | ||
| Summary | 290 | ||
| Glossary | 290 | ||
| Solved Problems | 291 | ||
| Self-Test | 295 | ||
| Discussion Questions and Problems | 296 | ||
| Case Study: Mexicana Wire Winding, Inc. | 304 | ||
| Bibliography | 306 | ||
| Chapter 8: Linear Programming Applications | 307 | ||
| 8.1. Marketing Applications | 307 | ||
| Media Selection | 307 | ||
| Marketing Research | 309 | ||
| 8.2. Manufacturing Applications | 311 | ||
| Production Mix | 311 | ||
| Production Scheduling | 313 | ||
| 8.3. Employee Scheduling Applications | 317 | ||
| Labor Planning | 317 | ||
| 8.4. Financial Applications | 318 | ||
| Portfolio Selection | 318 | ||
| Truck Loading Problem | 321 | ||
| 8.5. Ingredient Blending Applications | 323 | ||
| Diet Problems | 323 | ||
| Ingredient Mix and Blending Problems | 324 | ||
| 8.6. Other Linear Programming Applications | 326 | ||
| Summary | 328 | ||
| Self-Test | 328 | ||
| Problems | 329 | ||
| Case Study: Cable & Moore | 336 | ||
| Bibliography | 336 | ||
| Chapter 9: Transportation, Assignment, and Network Models | 337 | ||
| 9.1. The Transportation Problem | 338 | ||
| Linear Program for the Transportation Example | 338 | ||
| Solving Transportation Problems Using Computer Software | 339 | ||
| A General LP Model for Transportation Problems | 340 | ||
| Facility Location Analysis | 341 | ||
| 9.2. The Assignment Problem | 343 | ||
| Linear Program for Assignment Example | 343 | ||
| 9.3. The Transshipment Problem | 345 | ||
| Linear Program for Transshipment Example | 345 | ||
| 9.4. Maximal-Flow Problem | 348 | ||
| Example | 348 | ||
| 9.5. Shortest-Route Problem | 350 | ||
| 9.6. Minimal-Spanning Tree Problem | 352 | ||
| Summary | 355 | ||
| Glossary | 356 | ||
| Solved Problems | 356 | ||
| Self-Test | 358 | ||
| Discussion Questions and Problems | 359 | ||
| Case Study: Andrew–Carter, Inc. | 370 | ||
| Case Study: Northeastern Airlines | 371 | ||
| Case Study: Southwestern University Traffic Problems | 372 | ||
| Bibliography | 373 | ||
| Appendix 9.1: Using QM for Windows | 373 | ||
| Chapter 10: Integer Programming, Goal Programming, and Nonlinear Programming | 375 | ||
| 10.1. Integer Programming | 376 | ||
| Harrison Electric Company Example of Integer Programming | 376 | ||
| Using Software to Solve the Harrison Integer Programming Problem | 378 | ||
| Mixed-Integer Programming Problem Example | 378 | ||
| 10.2. Modeling with 0–1 (Binary) Variables | 381 | ||
| Capital Budgeting Example | 382 | ||
| Limiting the Number of Alternatives Selected | 383 | ||
| Dependent Selections | 383 | ||
| Fixed-Charge Problem Example | 384 | ||
| Financial Investment Example | 385 | ||
| 10.3. Goal Programming | 386 | ||
| Example of Goal Programming: Harrison Electric Company Revisited | 387 | ||
| Extension to Equally Important Multiple Goals | 388 | ||
| Ranking Goals with Priority Levels | 389 | ||
| Goal Programming with Weighted Goals | 389 | ||
| 10.4. Nonlinear Programming | 390 | ||
| Nonlinear Objective Function and Linear Constraints | 391 | ||
| Both Nonlinear Objective Function and Nonlinear Constraints | 391 | ||
| Linear Objective Function with Nonlinear Constraints | 392 | ||
| Summary | 393 | ||
| Glossary | 393 | ||
| Solved Problems | 394 | ||
| Self-Test | 396 | ||
| Discussion Questions and Problems | 397 | ||
| Case Study: Schank Marketing Research | 402 | ||
| Case Study: Oakton River Bridge | 403 | ||
| Bibliography | 403 | ||
| Chapter 11: Project Management | 405 | ||
| 11.1. PERT/CPM | 407 | ||
| General Foundry Example of PERT/CPM | 407 | ||
| Drawing the PERT/CPM Network | 408 | ||
| Activity Times | 409 | ||
| How to Find the Critical Path | 410 | ||
| Probability of Project Completion | 413 | ||
| What PERT Was Able to Provide | 416 | ||
| Using Excel QM for the General Foundry Example | 416 | ||
| Sensitivity Analysis and Project Management | 417 | ||
| 11.2. PERT/Cost | 418 | ||
| Planning and Scheduling Project Costs: Budgeting Process | 418 | ||
| Monitoring and Controlling Project Costs | 421 | ||
| 11.3. Project Crashing | 423 | ||
| General Foundry Example | 424 | ||
| Project Crashing with Linear Programming | 425 | ||
| 11.4. Other Topics in Project Management | 428 | ||
| Subprojects | 428 | ||
| Milestones | 428 | ||
| Resource Leveling | 428 | ||
| Software | 428 | ||
| Summary | 428 | ||
| Glossary | 428 | ||
| Key Equations | 429 | ||
| Solved Problems | 430 | ||
| Self-Test | 432 | ||
| Discussion Questions and Problems | 433 | ||
| Case Study: Southwestern University Stadium Construction | 440 | ||
| Case Study: Family Planning Research Center of Nigeria | 441 | ||
| Bibliography | 442 | ||
| Appendix 11.1: Project Management with QM for Windows | 442 | ||
| Chapter 12: Waiting Lines and Queuing Theory Models | 445 | ||
| 12.1. Waiting Line Costs | 446 | ||
| Three Rivers Shipping Company Example | 446 | ||
| 12.2. Characteristics of a Queuing System | 447 | ||
| Arrival Characteristics | 447 | ||
| Waiting Line Characteristics | 448 | ||
| Service Facility Characteristics | 448 | ||
| Identifying Models Using Kendall Notation | 449 | ||
| 12.3. Single-Channel Queuing Model with Poisson Arrivals and Exponential Service Times (M/M/1) | 452 | ||
| Assumptions of the Model | 452 | ||
| Queuing Equations | 452 | ||
| Arnold’s Muffler Shop Case | 453 | ||
| Enhancing the Queuing Environment | 456 | ||
| 12.4. Multichannel Queuing Model with Poisson Arrivals and Exponential Service Times (M/M/m) | 457 | ||
| Equations for the Multichannel Queuing Model | 457 | ||
| Arnold’s Muffler Shop Revisited | 458 | ||
| 12.5. Constant Service Time Model (M/D/1) | 460 | ||
| Equations for the Constant Service Time Model | 460 | ||
| Garcia-Golding Recycling, Inc. | 461 | ||
| 12.6. Finite Population Model (M/M/1 with Finite Source) | 461 | ||
| Equations for the Finite Population Model | 462 | ||
| Department of Commerce Example | 462 | ||
| 12.7. Some General Operating Characteristic Relationships | 463 | ||
| 12.8. More Complex Queuing Models and the Use of Simulation | 464 | ||
| Summary | 464 | ||
| Glossary | 465 | ||
| Key Equations | 465 | ||
| Solved Problems | 467 | ||
| Self-Test | 469 | ||
| Discussion Questions and Problems | 470 | ||
| Case Study: New England Foundry | 475 | ||
| Case Study: Winter Park Hotel | 477 | ||
| Bibliography | 477 | ||
| Appendix 12.1: Using QM for Windows | 478 | ||
| Chapter 13: Simulation Modeling | 479 | ||
| 13.1. Advantages and Disadvantages of Simulation | 480 | ||
| 13.2. Monte Carlo Simulation | 481 | ||
| Harry’s Auto Tire Example | 482 | ||
| Using QM for Windows for Simulation | 486 | ||
| Simulation with Excel Spreadsheets | 487 | ||
| 13.3. Simulation and Inventory Analysis | 489 | ||
| Simkin’s Hardware Store | 490 | ||
| Analyzing Simkin’s Inventory Costs | 493 | ||
| 13.4. Simulation of a Queuing Problem | 494 | ||
| Port of New Orleans | 494 | ||
| Using Excel to Simulate the Port of New Orleans Queuing Problem | 496 | ||
| 13.5. Simulation Model for a Maintenance Policy | 497 | ||
| Three Hills Power Company | 497 | ||
| Cost Analysis of the Simulation | 499 | ||
| 13.6. Other Simulation Issues | 502 | ||
| Two Other Types of Simulation Models | 502 | ||
| Verification and Validation | 503 | ||
| Role of Computers in Simulation | 503 | ||
| Summary | 504 | ||
| Glossary | 504 | ||
| Solved Problems | 505 | ||
| Self-Test | 507 | ||
| Discussion Questions and Problems | 508 | ||
| Case Study: Alabama Airlines | 514 | ||
| Case Study: Statewide Development Corporation | 515 | ||
| Case Study: FB Badpoore Aerospace | 516 | ||
| Bibliography | 518 | ||
| Chapter 14: Markov Analysis | 519 | ||
| 14.1. States and State Probabilities | 520 | ||
| The Vector of State Probabilities for Grocery Store Example | 521 | ||
| 14.2. Matrix of Transition Probabilities | 522 | ||
| Transition Probabilities for Grocery Store Example | 522 | ||
| 14.3. Predicting Future Market Shares | 523 | ||
| 14.4. Markov Analysis of Machine Operations | 524 | ||
| 14.5. Equilibrium Conditions | 525 | ||
| 14.6. Absorbing States and the Fundamental Matrix: Accounts Receivable Application | 528 | ||
| Summary | 532 | ||
| Glossary | 532 | ||
| Key Equations | 532 | ||
| Solved Problems | 533 | ||
| Self-Test | 536 | ||
| Discussion Questions and Problems | 537 | ||
| Case Study: Rentall Trucks | 541 | ||
| Bibliography | 543 | ||
| Appendix 14.1: Markov Analysis with QM for Windows | 543 | ||
| Appendix 14.2: Markov Analysis with Excel | 544 | ||
| Chapter 15: Statistical Quality Control | 547 | ||
| 15.1. Defining Quality and TQM | 547 | ||
| 15.2. Statistical Process Control | 549 | ||
| Variability in the Process | 549 | ||
| 15.3. Control Charts for Variables | 550 | ||
| The Central Limit Theorem | 551 | ||
| Setting x-Chart Limits | 552 | ||
| Setting Range Chart Limits | 554 | ||
| 15.4. Control Charts for Attributes | 555 | ||
| p-Charts | 555 | ||
| c-Charts | 557 | ||
| Summary | 559 | ||
| Glossary | 559 | ||
| Key Equations | 559 | ||
| Solved Problems | 560 | ||
| Self-Test | 561 | ||
| Discussion Questions and Problems | 561 | ||
| Bibliography | 564 | ||
| Appendix 15.1: Using QM for Windows for SPC | 565 | ||
| Appendices | 567 | ||
| Appendix A: Areas Under the Standard Normal Curve | 568 | ||
| Appendix B: Binomial Probabilities | 570 | ||
| Appendix C: Values of e for Use in the Poisson Distribution | 575 | ||
| Appendix D: F Distribution Values | 576 | ||
| Appendix E: Using POM-QM for Windows | 578 | ||
| Appendix F: Using Excel QM and Excel Add-Ins | 581 | ||
| Appendix G: Solutions to Selected Problems | 582 | ||
| Appendix H: Solutions to Self-Tests | 586 | ||
| Index | 589 | ||
| Back Cover | Back Cover |